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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI FREE CHAPTER 2. Chapter 2: Building Blocks of Intelligent Applications 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

Baselining

Baselining, in the context of GenAI, refers to the process of defining a standard or a reference output for the AI model to compare future outputs. This standard serves as a crucial benchmark for evaluating the model’s performance, consistency, and improvements over time. By establishing a baseline, developers and stakeholders can objectively measure how the AI performs relative to a predefined set of expectations, ensuring that the model meets and maintains desired standards.

In GenAI, baselining is essential for several reasons. Firstly, it provides a clear metric for assessing the quality and performance of the AI model. Secondly, it helps in tracking the model’s progress and improvements over time. Finally, baselining is a tool to help ensure consistency in the model’s outputs, via detection of output variability. All of these are vital for maintaining reliability and trust in the AI system.

The aspects of the AI model that can be baselined...

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